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Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

  • Ruben van de Geer*
  • , Arnoud V. den Boer
  • , Christopher Bayliss
  • , Christine S.M. Currie
  • , Andria Ellina
  • , Malte Esders
  • , Alwin Haensel
  • , Xiao Lei
  • , Kyle D.S. Maclean
  • , Antonio Martinez-Sykora
  • , Asbjørn Nilsen Riseth
  • , Fredrik Ødegaard
  • , Simos Zachariades
  • *Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29–30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

Original languageEnglish
Pages (from-to)185–203
Number of pages19
JournalJournal of Revenue and Pricing Management
Volume18
Issue number3
Early online date16 Oct 2018
DOIs
Publication statusPublished - 1 Jun 2019

Funding

Acknowledgements Chris Bayliss and Christine Currie were funded by the EPSRC under Grant Number EP/N006461/1. Andria Ellina and Simos Zachariades were part funded by EPSRC as part of their PhD studentships. Asbjørn Nilsen Riseth was partially funded by EPSRC Grant EP/L015803/1.

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/N006461/1, EP/L015803/1

    Keywords

    • Competition
    • Dynamic pricing
    • Learning
    • Numerical performance

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